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检索条件"任意字段=Multisensor. Multisource Information Fusion: Architectures, Algorithms, and Applications 2003"

共 284 条记录，以下是91-100 订阅

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Multi-Layered Context Impact Modulation for Enhanced Focus of Attention of Situational Awareness in Persistent Surveillance Systems

Multi-Layered Context Impact Modulation for Enhanced Focus o...

引用

Conference on multisensor. multisource information fusion - architectures, algorithms and applications 2010

作者： Shirkhodaie, Amir Rababaah, Haroun Tennessee State Univ Dept Mech & Mfg Engn Ctr Excellence Battlefield Sensor Fus Nashville TN 37209 USA

ISBN: (纸本)9780819481740

This paper presents a Multi-Layered Context Impact Modulation (MCIM) technique for persistent surveillance systems (PSS) and discusses its layered architecture for different context modulations including: spatial, temporal, sensor reliability, human presence, and environmental modulations. This paper also presents a fusion model for enhancement of focus of attention at the common operation picture (COP). The fusion model combines all the impacts from the different MCIM layers onto one unified modulated map. To test and evaluate the performance of MCIM, several experiments were conducted to modulate interaction of humans and vehicles which exhibit various normal and suspicious behaviors. The experimental results show strength of this approach in correctly modulating different suspicious situations with higher degree of certainty.

关键词： Persistent Surveillance Systems Situation Assessment Context Impact Layered Modulation Energy Logic fusion Engine Large-scale Sensor Networks Soft fusion

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Product-based multiresolution image fusion

Product-based multiresolution image fusion

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Conference on multisensor. multisource information fusion

作者： Ostrem, T Kozaitis, SP Florida Inst Technol Dept Elect & Comp Engn Melbourne FL 32901 USA

ISBN: (纸本)0819457981

We combined images from different sensors that were enhanced by multiscale products of the wavelet coefficients. Using the wavelet transform, we used a multiresolution analysis to form products of coefficients across scales. Then, a fusion rule was applied to the product images to determine how the original images could be combined. Using this approach, we were able to decrease the sensitivity of the fusion process to noise.

关键词： multiresolution image processing sensor fusion wavelet transform

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Novel classification fusion techniques for military target classification

Novel classification fusion techniques for military target c...

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Conference on multisensor. multisource information fusion

作者： Liu, Ting Lampropoulos, George A. AUG Signals 73 Richmond St W Toronto ON Canada

ISBN: (纸本)9780819466938

The objective of this paper is to develop novel classification structures for military targets detection and recognition by employing different fusion techniques. In real applications, the great diversity of materials in the background areas and the similarity between the background and target signatures result in high false alarm rates and large miss classification errors. In this paper, three new systems are proposed using different fusion techniques: pixel level fusion, decision fusion, and classification fusion employing confidence vectors. These new developed systems are tested using an experimental data to show its effectiveness.

关键词： hyperspectral detection classification fusion confusion matrix false alarm

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Combining belief functions and fuzzy membership functions

Combining belief functions and fuzzy membership functions

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Conference on multisensor. multisource information fusion - architectures, algorithms and applications 2003

作者： Florea, MC Jousselme, AL Grenier, D Bossé, T Univ Laval Quebec City PQ G1K 7P4 Canada

ISBN: (纸本)0819449598

In several practical applications of data fusion and more precisely in object identification problems, we need to combine imperfect information coming from different sources (sensors, humans, etc.), the resulting uncertainty being naturally of different kinds. In particular, one information could naturally been expressed by a membership function while the other could best be represented by a belief function. Usually, information modeled in the fuzzy sets formalism (by a membership function) concerns attributes like speed, length, or Radar Cross Section whose domains of definition are continuous. However, the object identification problem refers to a discrete and finite framework (the number of objects in the data base is finite and known). This implies thus a natural but unavoidable change of domain. To be able to respect the intrinsic characteristic of uncertainty arising from the different sources and fuse it in order to identify an object among a list of possible ones in the data base, we need (1) to use a unified framework where both fuzzy sets and belief functions can be expressed, (2) to respect the natural discretization of the membership function through the change of domain (from attribute domain to frame of discernment). In this paper, we propose to represent both fuzzy sets and belief function by random sets. While the link between belief functions and random sets, is direct, transforming fuzzy sets into random sets involves the use of a-cuts for the construction of the focal elements. This transformation usually generates a large number of focal elements often unmanageable in a fusion process. We propose a way to reduce the number of focal elements based on some parameters like the desired number of focal elements, the acceptable distance from the approximated random set to the original discrete one, or the acceptable loss of information.

关键词： random sets fuzzy sets belief functions object identification information fusion

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INFORM Lab: a testbed for high-level information fusion and resource management

INFORM Lab: a testbed for high-level information fusion and ...

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Conference on multisensor. multisource information fusion - architectures, algorithms, and applications

作者： Valin, Pierre Guitouni, Adel Bosse, Eloi Wehn, Hans Happe, Jens Def R&D Canada Valcartier Decis Support Syst Sect C2 2459 Blvd Pie 11 Nord Quebec City PQ G3J 1X5 Canada MacDonald Dettwiler & Associates Ltd Richmond BC V6V 2J3 Canada

ISBN: (纸本)9780819486387

DRDC Valcartier and MDA have created an advanced simulation testbed for the purpose of evaluating the effectiveness of Network Enabled Operations in a Coastal Wide Area Surveillance situation, with algorithms provided by several universities. This INFORM Lab testbed allows experimenting with high-level distributed information fusion, dynamic resource management and configuration management, given multiple constraints on the resources and their communications networks. This paper describes the architecture of INFORM Lab, the essential concepts of goals and situation evidence, a selected set of algorithms for distributed information fusion and dynamic resource management, as well as auto-configurable information fusion architectures. The testbed provides general services which include a multilayer plug-and-play architecture, and a general multi-agent framework based on John Boyd's OODA loop. The testbed's performance is demonstrated on 2 types of scenarios/vignettes for 1) cooperative search-and-rescue efforts, and 2) a noncooperative smuggling scenario involving many target ships and various methods of deceit. For each mission, an appropriate subset of Canadian airborne and naval platforms are dispatched to collect situation evidence, which is fused, and then used to modify the platform trajectories for the most efficient collection of further situation evidence. These platforms are fusion nodes which obey a Command and Control node hierarchy.

关键词： surveillance information fusion distributed fusion software agents simulation testbed

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A study of integration methods of aerial imagery and LIDAR data for high level of automation in 3D building reconstruction

A study of integration methods of aerial imagery and LIDAR d...

引用

Conference on multisensor. multisource information fusion - architectures, algorithms and applications 2003

作者： Seo, SY Schenk, T Ohio State Univ Dept Civil & Environm Engn & Geodet Sci Columbus OH 43210 USA

ISBN: (纸本)0819449598

This paper describes integration methods to increase the level of automation in building reconstruction. Aerial imagery has been used as a major source in mapping fields and, in recent years, LIDAR data became popular as another type of mapping resources. Regarding to their performances, aerial imagery has abilities to delineate object boundaries but leaves many missing parts of boundaries during feature extraction. LIDAR data provide direct information about heights of object surfaces but have limitation for boundary localization. Efficient methods using complementary characteristics of two sensors are described to generate hypotheses of building boundaries and localize the object features. Tree structures for grid contours of LIDAR data are used for interpretation of contours. Buildings are recognized by analyzing the contour trees and modeled with surface patches from LIDAR data. Hypotheses of building models are generated as combination of wing models and verified by assessing the consistency between the corresponding data sets. Experiments using aerial imagery and laser data are presented. Our approach shows that the building boundaries are successfully recognized through our contour analysis approach and the inference from contours and our modeling method using wing model increase the level of automation in hypothesis generation/verification steps.

关键词： aerial imagery LIDAR contour model wing model building recognition building reconstruction sensor fusion co-registration

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Spatial Voting For Automatic Feature Selection, fusion, and Visualization

Spatial Voting For Automatic Feature Selection, Fusion, and ...

引用

Conference on multisensor. multisource information fusion - architectures, algorithms, and applications

作者： Jaenisch, Holger Handley, James Licht Strahl Engineering Inc. (United States) Johns Hopkins Univ. (United States) Alabama A&M Univ. (United States)

ISBN: (纸本)9780819495471

We present a novel feature selection, fusion, and visualization utility using Spatial Voting (SV). This SV feature optimization utility is designed to be an off-line stand-alone utility to help an investigator find useful feature pairs for cluster analysis and lineage identification. The analysis can be used to enable the analyst to vary parameters manually and explore the best combination that yields visually appealing or significant groups or spreading of data points depending on the planned use of the analysis downstream. Several different criteria are available to the user in order to determine the best SV grid size and feature pair including minimizing zeros, minimizing covariance, balanced minimum covariance, or the maximization of one of eight different scoring metrics: Containment, Rand Index, Purity, Precision, Recall, F-Score, Normalized Mutual information (NMI), and Adjusted Rand Index (ARI). The tool that is described in this work facilitates this analysis and makes it simple, efficient, and interactive if the analyst so desires.

关键词： Spatial Voting Automatic Feature Selection fusion Visualization NMI Rand Index ARI

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Probabilistic Multi-source Multi-INT Intel fusion Benefit Analysis

Probabilistic Multi-source Multi-INT Intel Fusion Benefit An...

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Conference on multisensor. multisource information fusion - architectures, algorithms, and applications

作者： Wisniewski, Ski (David) Hershey, Paul Raytheon 302 Sci Pk Rd State Coll PA 16802 USA Raytheon Dulles VA 20166 USA

ISBN: (纸本)9781628410587

The Process for Probabilistic Multi-Source Multi-INT Intel fusion Benefit Analysis** (PIFBA) provides a methodology for statistically computing the probability of detecting, locating, and tracking objects in order to assess current capabilities or the benefit of adding or removing ISR assets in order to obtain the statistically optimal result. The PIFBA process defines the approach to calculate the probabilistic benefits or rewards associated with integrating or fusing multisource multi-INT products across a wide range of platforms, sensors, environmental conditions and target objects. This process applies to analyzing ISR capabilities, effectiveness, and gaps, as well as the benefits of applying existing or new technology and tactics. The PIFBA process was designed to answer the following questions: Based on the defined ISR assets - what is the probability that we know a piece of Intel with sufficient accuracy and timeliness to be of value to the analyst or warfighter? What is the benefit of adding new data via additional platforms, sensors, or processing? What is the benefit of adding new systems or technology and to what degree of performance must they exhibit in order to affect the statistical outcome?

关键词： Multi-Source Multi-INT fusion Automated Decision Aides ISR Statistical Computation Probability of Detecting, Locating, and Tracking Processing, Exploitation, Dissemination (PED)

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Hybrid fusion and demosaicing algorithm with near-infrared image

Hybrid fusion and demosaicing algorithm with near-infrared i...

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Conference on multisensor. multisource information fusion - architectures, algorithms, and applications

作者： Luo, X. Y. Zhang, J. Dai, Q. H. Beihang Univ Image Proc Ctr Sch Astronaut Beijing 100191 Peoples R China Beihang Univ Sch Elect & Informat Engn Natl Key Lab CNS ATM Beijing 100191 Peoples R China Tsinghua Univ Tsinghua Natl Lab Informat Sci & Technol Dept Automat Beijing 100084 Peoples R China

ISBN: (纸本)9781628410587

In this paper, we propose a unified framework for color filter array (CFA) interpolation and visible/NIR image combination. The proposed method aims to reconstruct a high quality image from raw CFA data and the corresponding NIR image, similar to a multi-spectral fusion of color and NIR images. Based on NIR image, we impose a sparse constraint of gradient difference to modify the traditional color interpolation. The experiments indicate the effectiveness of our hybrid scheme to acquire joint color and NIR information in real-time, and show that this hybrid process can generate a better color image when compared with treating interpolation and fusion separately.

关键词： Color filter array demosaicing hybrid interpolation image fusion

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PASES: Combining radar and video sensing for improved pedestrian safety

PASES: Combining radar and video sensing for improved pedest...

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Conference on multisensor. multisource information fusion - architectures, algorithms, and applications

作者： Chenault, David B. Edmondson, Richard P. Polaris Sensor Technol Inc 200 West Side SqSuite 320 Huntsville AL 35801 USA

ISBN: (纸本)9781510601130

Polaris Sensor Technologies reports on the development of Pedestrian Automated System for Enforcement and Safety (PASES), a radar and video based system used to monitor vehicle and pedestrian traffic with the intent of improving pedestrian safety. Data is fused from a system of multiple sensors and multiple sensor modalities to identify vehicular violations of pedestrian right of way. A focus was placed on the selection of low cost COTS sensors to make the system more widely available to state and local DOTs with limited budgets. applications include automated enforcement, adaptive traffic control, and improved intersection and crosswalk design based on high quality data available for traffic engineering. We discuss early results with high fidelity sensors, and the performance trades made in order to make the system affordable. A discussion of the system processing architecture is included which highlights the treatment of each sensor data type, and the means of combining the processed data products into state information related to traffic incidents involving vehicles and pedestrians.

关键词： Pedestrian radar video tracking sensor fusion

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